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craftaxenv.github.io

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Existing benchmarks for open-ended learning are either too slow or too simple. Craftax is both fast and complicated. We hope that this will allow researchers without access to industrial compute to investigate learning in an open-ended environment with an ease that was not previously possible. Progress in reinforcement learning (RL) algorithms is driven in large part by the development and adoption of suitable benchmarks. In the effort towards increasingly general agents, there has arisen a community focused on benchmarks that exhibit more open-ended dynamics, in the form of procedural world generation, skill acquisition and reuse, long term dependencies and continual learning. This has motivated the development of environments like MALMO (Minecraft), The NetHack Learning Environment, MiniHack and Crafter. However, slow runtime has rendered them inaccessible to current methods without large-scale computational resources, limiting their practicality to the research community. We present

The Craftax Benchmark --> Authors --> Michael Matthews 1 --> Michael Beukman 1 Benjamin Ellis 1,2 Mikayel Samvelyan 3 Matthew Jackson 1,2 --> Samuel Coward 1 Jakob Foerster 1 1 FLAIR , University of Oxford 2 WhiRL , University of Oxford 3 DARK , University College London arXiv GitHub TL;DR Existing benchmarks for open-ended learning are either too slow or too simple. Craftax is both fast and complicated. We hope that this will allow researchers without access to industrial compute to investigate learning in an open-ended environment with an ease that was not previously possible. Introduction P

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